A Genetic-Based Iterative Quantile Regression Algorithm for Analyzing Fatigue Curves
Article first published online: 10 JAN 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Quality and Reliability Engineering International
Volume 28, Issue 8, pages 897–909, December 2012
How to Cite
Park, J. I., Kim, N. and Bae, S. J. (2012), A Genetic-Based Iterative Quantile Regression Algorithm for Analyzing Fatigue Curves. Qual. Reliab. Engng. Int., 28: 897–909. doi: 10.1002/qre.1280
- Issue published online: 26 NOV 2012
- Article first published online: 10 JAN 2012
- fatigue curves;
- iterative quantile regression;
- genetic algorithms;
- structural risk minimization;
- censored data;
- general approximate cross-validation error
Accurate prediction of fatigue failure times of materials such as fracture and plastic deformation at various stress ranges has a strong bearing on practical fatigue design of materials. In this study, we propose a novel genetic-based iterative quantile regression (GA-IQR) algorithm for analyzing fatigue curves that represent a nonlinear relationship between a given stress amplitude and fatigue life. We reduce the problem to a linear framework and develop the iterative algorithm for determining the model coefficients including unknown fatigue limits. The procedure keeps updating the estimates in a direction to reduce its resulting error. Also, our approach benefits from the population-based stochastic search of the genetic algorithms so that the algorithm becomes less sensitive to its initialization. Compared with conventional approaches, the proposed GA-IQR requires fewer assumptions to develop fatigue model, capable of exploring the data structure in a relatively flexible manner. All procedures and calculations are quite straightforward, such that the proposed quantile regression model has a high potential value in a wide range of applications for exploring nonlinear relationships with lifetime data. Computational results for real data sets found in the literature present good evidences to support the argument. Copyright © 2012 John Wiley & Sons, Ltd.